Main Text Figures
comb_epr_plot = comb_preds %>%
filter(metric == "EPR") %>%
ggplot() +
geom_point(aes(temp, rate, colour = curve_id),
filter(comb_d, metric == "EPR"),
size = 1.5, alpha = 0.6,
position = position_jitter(width = 0.5, height = 0)) +
geom_ribbon(aes(temp, ymin = conf_lower, ymax = conf_upper, group = curve_id),
filter(comb_boot_conf_preds, metric == "EPR"),
fill = 'grey60', alpha = 0.3) +
geom_line(aes(temp, .fitted, col = curve_id), linewidth = 2) +
scale_colour_manual(values = comb_colors) +
labs(x = "",
y = "Egg Production \n(eggs/female/day)",
colour = "Month") +
theme_matt(base_size = 12)
comb_hs_plot = comb_preds %>%
filter(metric == "HF") %>%
ggplot() +
geom_point(aes(temp, rate, colour = curve_id), filter(comb_d, metric == "HF"), size = 1.5, alpha = 0.6,
position = position_jitter(width = 0.5, height = 0)) +
geom_ribbon(aes(temp, ymin = conf_lower, ymax = conf_upper, group = curve_id), filter(comb_boot_conf_preds, metric == "HF"), fill = 'grey60', alpha = 0.3) +
geom_line(aes(temp, .fitted, col = curve_id), linewidth = 2) +
#scale_color_brewer(type = "div", palette = 5, direction = -1) +
#scale_color_viridis_d(option = "mako") +
scale_colour_manual(values = comb_colors) +
labs(x = "",
y = "Hatching Success \n(%)",
colour = "Month") +
theme_matt(base_size = 12)
comb_rf_plot = comb_preds %>%
filter(metric == "RF") %>%
ggplot() +
geom_point(aes(temp, rate, colour = curve_id), filter(comb_d, metric == "RF"), size = 1.5, alpha = 0.6,
position = position_jitter(width = 0.5, height = 0)) +
geom_ribbon(aes(temp, ymin = conf_lower, ymax = conf_upper, group = curve_id), filter(comb_boot_conf_preds, metric == "RF"), fill = 'grey60', alpha = 0.3) +
geom_line(aes(temp, .fitted, col = curve_id), linewidth = 2) +
#scale_color_brewer(type = "div", palette = 5, direction = -1) +
#scale_color_viridis_d(option = "mako") +
scale_colour_manual(values = comb_colors) +
labs(x = "Temperature (°C)",
y = "Offspring Production \n(offspring/female/day)",
colour = "Month") +
theme_matt(base_size = 12)
comb_tsc = ggplot(comb_surv, aes(x=Temp, y=Surv, colour=Month)) +
geom_point(size=1.5, position=position_jitter(width=0.1, height=0.03)) +
xlab("Temperature (°C)")+
ylab("Survivorship \n(proportion survived)")+
labs(colour = "Month") +
geom_hline(yintercept = 0.5, linetype = "dashed") +
geom_smooth(method = "glm", method.args = list(family = "binomial"), se=T, linewidth = 2) +
scale_y_continuous(breaks = c(0,1)) +
#scale_color_brewer(type = "div", palette = 5, direction = -1) +
#scale_color_viridis_d(option = "mako") +
scale_colour_manual(values = comb_colors) +
theme_matt(base_size = 12)
ggarrange(comb_epr_plot, comb_hs_plot, comb_rf_plot, comb_tsc,
ncol = 2, nrow = 2,
common.legend = T, legend = "bottom",
labels = "AUTO", vjust = 1)

# ggarrange(comb_epr_plot, comb_hs_plot, comb_rf_plot, comb_tsc, nrow = 1,
# common.legend = T, legend = "bottom")
combined_opt_coll = comb_params %>%
filter(metric == "RF" & term == "topt") %>%
ggplot(aes(x = growth_temp, y = estimate, shape = species)) +
geom_smooth(data = filter(comb_params, metric == "RF" & term == "topt" & curve_id != "November_2"),
method = "lm", colour = "grey50") +
geom_point(size = 3, stroke = 1) +
scale_shape_manual(values = c(16,21)) +
ylab("Optimum (°C)") +
xlab("Collection Temperature (°C)") +
labs(colour = "Month") +
theme_matt(base_size = 12)
combined_opt_diff = comb_params %>%
filter(metric == "RF" & term == "topt") %>%
ggplot(aes(x = growth_temp, y = margin, shape = species)) +
geom_hline(yintercept = 0, linewidth =1, linetype = "dashed") +
geom_smooth(data = filter(comb_params, metric == "RF" & term == "topt" & curve_id != "November_2"),
method = "lm", colour = "grey50") +
geom_point(size = 3, stroke = 1) +
ylab("Margin (°C)") +
xlab("Collection Temperature (°C)") +
scale_shape_manual(values = c(16,21)) +
theme_matt(base_size = 12)
combined_ld_coll = ggplot(combined_tolerance, aes(x = Coll_temp, y = LD50, shape = species)) +
geom_smooth(method = "lm", colour = "grey50") +
geom_point(size = 3, stroke = 1) +
scale_shape_manual(values = c(16,21)) +
ylab("Thermal Tolerance (°C)") +
xlab("Collection Temperature (°C)") +
theme_matt(base_size = 12)
combined_ld_diff = ggplot(combined_tolerance, aes(x = Coll_temp, y = margin, shape = species)) +
geom_smooth(method = "lm", colour = "grey50") +
geom_point(size = 3, stroke = 1) +
scale_shape_manual(values = c(16,21)) +
ylab("Warming Tolerance (°C)") +
xlab("Collection Temperature (°C)") +
theme_matt(base_size = 12)
ggarrange(combined_opt_coll, combined_opt_diff, combined_ld_coll, combined_ld_diff, ncol = 2, nrow = 2, common.legend = T,
legend = "bottom", labels = "AUTO")

F0_hatched.model = lme4::glmer(data = f0_model_data, family = poisson,
Hatched ~ Treatment * Day +
(1|Month) + (1|female_ID))
summary(F0_hatched.model)
## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: poisson ( log )
## Formula: Hatched ~ Treatment * Day + (1 | Month) + (1 | female_ID)
## Data: f0_model_data
##
## AIC BIC logLik deviance df.resid
## 8235.0 8259.5 -4111.5 8223.0 426
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.9713 -2.0464 -0.4943 2.1551 7.5467
##
## Random effects:
## Groups Name Variance Std.Dev.
## female_ID (Intercept) 1.468 1.211
## Month (Intercept) 2.239 1.496
## Number of obs: 432, groups: female_ID, 216; Month, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.30143 0.87265 3.783 0.000155 ***
## TreatmentHeatwave 0.22773 0.16967 1.342 0.179539
## Day5_to_7 -0.37316 0.01665 -22.413 < 2e-16 ***
## TreatmentHeatwave:Day5_to_7 -0.22533 0.02352 -9.581 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) TrtmnH Dy5__7
## TretmntHtwv -0.098
## Day5_to_7 -0.008 0.040
## TrtmH:D5__7 0.006 -0.053 -0.708
car::Anova(F0_hatched.model, type = "III")
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: Hatched
## Chisq Df Pr(>Chisq)
## (Intercept) 14.3129 1 0.0001548 ***
## Treatment 1.8014 1 0.1795386
## Day 502.3581 1 < 2.2e-16 ***
## Treatment:Day 91.7891 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
f0_model_females = F0_epr %>%
group_by(Month, Treatment, Female) %>%
count() %>%
filter(n == 2) %>%
mutate('female_ID' = paste(Month, Treatment, Female, sep = "_"))
f0_model_data = F0_epr %>%
mutate('female_ID' = paste(Month, Treatment, Female, sep = "_")) %>%
filter(female_ID %in% f0_model_females$female_ID)
f0_summary = f0_model_data %>%
group_by(Month, Treatment, Day) %>%
summarise("mean_hatched" = mean(Hatched, na.rm = T),
"hatched_se" = st.err(Hatched, na.rm = T)) %>%
ungroup() %>%
mutate(Day = if_else(Day == "1_to_3", "Short", "Long"),
Month = fct_relevel(Month, "June", "August", "November"),
Day = fct_relevel(Day, "Short", "Long"))
ggplot(f0_summary,
aes(x = Month, y = mean_hatched, colour = Treatment)) +
facet_grid(.~Day, scales = "free_y") +
geom_hline(yintercept = 0) +
geom_errorbar(aes(ymin = mean_hatched - hatched_se,
ymax = mean_hatched + hatched_se),
width = 0.2, linewidth = 1,
position = position_dodge(width = 0.3)) +
geom_point(size = 4, position = position_dodge(width = 0.3)) +
scale_colour_manual(values = c("Heatwave" = "coral3", "Control" = "skyblue3")) +
labs(x = "Month",
y = "Mean Offspring Production \n(offspring/female/day)") +
theme_matt() +
theme(panel.border = element_rect(fill = NA, colour = "black"),
legend.position = "bottom",
axis.text.x = element_text(angle = 300, hjust = 0, vjust = 0.5))

f1_model_data = F1_epr %>%
mutate(Offspring_temp = as.factor(Offspring_temp)) %>%
ungroup() %>%
mutate(Day = if_else(Day == "1_to_3", "Short", "Long"),
Month = fct_relevel(Month, "June", "August", "November"),
Day = fct_relevel(Day, "Short", "Long"))
ggplot(f1_model_data,
aes(x = Offspring_temp, y = Hatched, colour = Parental_treatment)) +
facet_grid(Month~Day) +
#geom_violin(position = position_dodge(width = 1)) +
geom_boxplot(position = position_dodge(width = 0.5),
width = 0.3) +
geom_point(position = position_dodge(width = 0.5),
alpha = 0.5) +
scale_colour_manual(values = c("Heatwave" = "coral3", "Control" = "skyblue3")) +
scale_y_continuous(breaks = c(0, 100, 200)) +
labs(x = "Offspring Temp. (°C)",
y = "Offspring Production \n(offspring/female/day)",
colour = "Parental Treatment") +
theme_matt() +
theme(panel.border = element_rect(fill = NA, colour = "black"),
legend.position = "bottom")

F1_hatched.model = lme4::glmer(data = f1_model_data, family = poisson,
Hatched ~ Parental_treatment * Day * Offspring_temp +
(1 + Parental_treatment|Month))
summary(F1_hatched.model)
## Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
## Family: poisson ( log )
## Formula: Hatched ~ Parental_treatment * Day * Offspring_temp + (1 + Parental_treatment |
## Month)
## Data: f1_model_data
##
## AIC BIC logLik deviance df.resid
## 33587.5 33660.0 -16778.7 33557.5 916
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -12.080 -3.935 -1.187 2.948 27.215
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## Month (Intercept) 3.321 1.822
## Parental_treatmentHeatwave 3.064 1.750 -0.96
## Number of obs: 931, groups: Month, 3
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.60247 1.04241 1.537 0.124
## Parental_treatmentHeatwave 0.71588 1.00227 0.714 0.475
## DayLong -0.61233 0.05000 -12.247 <2e-16 ***
## Offspring_temp17 1.14693 0.03469 33.063 <2e-16 ***
## Offspring_temp22 1.02875 0.03468 29.664 <2e-16 ***
## Parental_treatmentHeatwave:DayLong -0.80342 0.09293 -8.646 <2e-16 ***
## Parental_treatmentHeatwave:Offspring_temp17 0.71131 0.05197 13.687 <2e-16 ***
## Parental_treatmentHeatwave:Offspring_temp22 0.54682 0.05247 10.421 <2e-16 ***
## DayLong:Offspring_temp17 1.21272 0.05456 22.229 <2e-16 ***
## DayLong:Offspring_temp22 0.82261 0.05566 14.780 <2e-16 ***
## Parental_treatmentHeatwave:DayLong:Offspring_temp17 -0.15317 0.09738 -1.573 0.116
## Parental_treatmentHeatwave:DayLong:Offspring_temp22 -0.05691 0.09930 -0.573 0.567
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Prnt_H DayLng Off_17 Off_22 Pr_H:DL P_H:O_1 P_H:O_2 DL:O_1 DL:O_2
## Prntl_trtmH -0.961
## DayLong -0.017 0.017
## Offsprng_17 -0.024 0.025 0.507
## Offsprng_22 -0.024 0.025 0.507 0.731
## Prntl_tH:DL 0.009 -0.024 -0.538 -0.273 -0.273
## Prnt_H:O_17 0.016 -0.042 -0.338 -0.667 -0.488 0.458
## Prnt_H:O_22 0.016 -0.042 -0.335 -0.483 -0.661 0.454 0.816
## DyLng:Of_17 0.015 -0.015 -0.916 -0.636 -0.465 0.493 0.424 0.307
## DyLng:Of_22 0.015 -0.016 -0.898 -0.455 -0.623 0.483 0.304 0.412 0.823
## P_H:DL:O_17 -0.008 0.022 0.513 0.356 0.260 -0.954 -0.532 -0.433 -0.560 -0.461
## P_H:DL:O_22 -0.008 0.022 0.503 0.255 0.349 -0.936 -0.429 -0.526 -0.461 -0.560
## P_H:DL:O_1
## Prntl_trtmH
## DayLong
## Offsprng_17
## Offsprng_22
## Prntl_tH:DL
## Prnt_H:O_17
## Prnt_H:O_22
## DyLng:Of_17
## DyLng:Of_22
## P_H:DL:O_17
## P_H:DL:O_22 0.893
coefficients(F1_hatched.model)
## $Month
## (Intercept) Parental_treatmentHeatwave DayLong Offspring_temp17 Offspring_temp22
## June 3.2357331 -0.2739582 -0.6123292 1.146931 1.028746
## August 2.5110937 -0.7530331 -0.6123292 1.146931 1.028746
## November -0.9361519 3.1716816 -0.6123292 1.146931 1.028746
## Parental_treatmentHeatwave:DayLong Parental_treatmentHeatwave:Offspring_temp17
## June -0.8034179 0.7113092
## August -0.8034179 0.7113092
## November -0.8034179 0.7113092
## Parental_treatmentHeatwave:Offspring_temp22 DayLong:Offspring_temp17
## June 0.5468181 1.212715
## August 0.5468181 1.212715
## November 0.5468181 1.212715
## DayLong:Offspring_temp22 Parental_treatmentHeatwave:DayLong:Offspring_temp17
## June 0.8226093 -0.1531655
## August 0.8226093 -0.1531655
## November 0.8226093 -0.1531655
## Parental_treatmentHeatwave:DayLong:Offspring_temp22
## June -0.05690774
## August -0.05690774
## November -0.05690774
##
## attr(,"class")
## [1] "coef.mer"
car::Anova(F1_hatched.model, type = "III")
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: Hatched
## Chisq Df Pr(>Chisq)
## (Intercept) 2.3632 1 0.12423
## Parental_treatment 0.5102 1 0.47507
## Day 149.9922 1 < 2e-16 ***
## Offspring_temp 1158.0413 2 < 2e-16 ***
## Parental_treatment:Day 74.7488 1 < 2e-16 ***
## Parental_treatment:Offspring_temp 189.0279 2 < 2e-16 ***
## Day:Offspring_temp 532.5149 2 < 2e-16 ***
## Parental_treatment:Day:Offspring_temp 5.8855 2 0.05272 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
f1_size_data = F1_fbs %>%
ungroup() %>%
mutate(Day = if_else(Day == "1_to_3", "Short", "Long"),
Month = fct_relevel(Month, "June", "August", "November"),
Day = fct_relevel(Day, "Short", "Long"))
F1_size.model = lme4::lmer(data = f1_size_data,
Size ~ Parental_treatment * Offspring_temp * Day * Month +
(1|Month))
summary(F1_size.model)
## Linear mixed model fit by REML ['lmerMod']
## Formula: Size ~ Parental_treatment * Offspring_temp * Day * Month + (1 | Month)
## Data: f1_size_data
##
## REML criterion at convergence: -2830
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5169 -0.6071 -0.0512 0.6534 3.7749
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.000417 0.02042
## Residual 0.001721 0.04149
## Number of obs: 864, groups: Month, 3
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1.1129676 0.0279917 39.761
## Parental_treatmentHeatwave -0.2021937 0.0314027 -6.439
## Offspring_temp -0.0116568 0.0010991 -10.606
## DayLong -0.0639884 0.0270852 -2.362
## MonthAugust -0.0778029 0.0406730 -1.913
## MonthNovember -0.1583042 0.0620470 -2.551
## Parental_treatmentHeatwave:Offspring_temp 0.0105033 0.0017407 6.034
## Parental_treatmentHeatwave:DayLong 0.1530333 0.0414772 3.690
## Offspring_temp:DayLong 0.0033498 0.0015625 2.144
## Parental_treatmentHeatwave:MonthAugust 0.1597975 0.0426050 3.751
## Parental_treatmentHeatwave:MonthNovember 0.0138210 0.0735478 0.188
## Offspring_temp:MonthAugust 0.0030094 0.0016350 1.841
## Offspring_temp:MonthNovember 0.0104002 0.0037188 2.797
## DayLong:MonthAugust -0.0208334 0.0394776 -0.528
## DayLong:MonthNovember 0.1684485 0.0627450 2.685
## Parental_treatmentHeatwave:Offspring_temp:DayLong -0.0074358 0.0023391 -3.179
## Parental_treatmentHeatwave:Offspring_temp:MonthAugust -0.0104701 0.0023926 -4.376
## Parental_treatmentHeatwave:Offspring_temp:MonthNovember -0.0008553 0.0044990 -0.190
## Parental_treatmentHeatwave:DayLong:MonthAugust -0.0655264 0.0576266 -1.137
## Parental_treatmentHeatwave:DayLong:MonthNovember 0.0366208 0.0858275 0.427
## Offspring_temp:DayLong:MonthAugust 0.0021581 0.0022665 0.952
## Offspring_temp:DayLong:MonthNovember -0.0099254 0.0040868 -2.429
## Parental_treatmentHeatwave:Offspring_temp:DayLong:MonthAugust 0.0029113 0.0032879 0.885
## Parental_treatmentHeatwave:Offspring_temp:DayLong:MonthNovember -0.0041042 0.0051223 -0.801
coefficients(F1_size.model)
## $Month
## (Intercept) Parental_treatmentHeatwave Offspring_temp DayLong MonthAugust
## June 1.112968 -0.2021937 -0.01165684 -0.06398839 -0.07780293
## August 1.112968 -0.2021937 -0.01165684 -0.06398839 -0.07780293
## November 1.112968 -0.2021937 -0.01165684 -0.06398839 -0.07780293
## MonthNovember Parental_treatmentHeatwave:Offspring_temp
## June -0.1583042 0.01050334
## August -0.1583042 0.01050334
## November -0.1583042 0.01050334
## Parental_treatmentHeatwave:DayLong Offspring_temp:DayLong
## June 0.1530333 0.003349841
## August 0.1530333 0.003349841
## November 0.1530333 0.003349841
## Parental_treatmentHeatwave:MonthAugust Parental_treatmentHeatwave:MonthNovember
## June 0.1597975 0.01382099
## August 0.1597975 0.01382099
## November 0.1597975 0.01382099
## Offspring_temp:MonthAugust Offspring_temp:MonthNovember DayLong:MonthAugust
## June 0.003009359 0.01040017 -0.02083344
## August 0.003009359 0.01040017 -0.02083344
## November 0.003009359 0.01040017 -0.02083344
## DayLong:MonthNovember Parental_treatmentHeatwave:Offspring_temp:DayLong
## June 0.1684485 -0.007435826
## August 0.1684485 -0.007435826
## November 0.1684485 -0.007435826
## Parental_treatmentHeatwave:Offspring_temp:MonthAugust
## June -0.01047015
## August -0.01047015
## November -0.01047015
## Parental_treatmentHeatwave:Offspring_temp:MonthNovember
## June -0.0008553403
## August -0.0008553403
## November -0.0008553403
## Parental_treatmentHeatwave:DayLong:MonthAugust
## June -0.06552641
## August -0.06552641
## November -0.06552641
## Parental_treatmentHeatwave:DayLong:MonthNovember Offspring_temp:DayLong:MonthAugust
## June 0.03662077 0.002158149
## August 0.03662077 0.002158149
## November 0.03662077 0.002158149
## Offspring_temp:DayLong:MonthNovember
## June -0.009925365
## August -0.009925365
## November -0.009925365
## Parental_treatmentHeatwave:Offspring_temp:DayLong:MonthAugust
## June 0.002911311
## August 0.002911311
## November 0.002911311
## Parental_treatmentHeatwave:Offspring_temp:DayLong:MonthNovember
## June -0.004104211
## August -0.004104211
## November -0.004104211
##
## attr(,"class")
## [1] "coef.mer"
car::Anova(F1_size.model, type = "III")
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: Size
## Chisq Df Pr(>Chisq)
## (Intercept) 1580.9097 1 < 2.2e-16 ***
## Parental_treatment 41.4574 1 1.205e-10 ***
## Offspring_temp 112.4837 1 < 2.2e-16 ***
## Day 5.5814 1 0.0181527 *
## Month 7.8995 2 0.0192594 *
## Parental_treatment:Offspring_temp 36.4100 1 1.599e-09 ***
## Parental_treatment:Day 13.6130 1 0.0002246 ***
## Offspring_temp:Day 4.5960 1 0.0320460 *
## Parental_treatment:Month 15.1607 2 0.0005104 ***
## Offspring_temp:Month 9.5403 2 0.0084791 **
## Day:Month 9.1255 2 0.0104334 *
## Parental_treatment:Offspring_temp:Day 10.1054 1 0.0014784 **
## Parental_treatment:Offspring_temp:Month 20.3284 2 3.852e-05 ***
## Parental_treatment:Day:Month 2.0620 2 0.3566473
## Offspring_temp:Day:Month 8.6232 2 0.0134123 *
## Parental_treatment:Offspring_temp:Day:Month 2.1096 2 0.3482539
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
day_cols = c("Short" = "grey80", "Long" = "grey30")
size_temp1 = ggplot(f1_size_data, aes(x = Offspring_temp, y = Size, colour = Parental_treatment)) +
facet_grid(Month~Day) +
geom_jitter(width = 0.5, size = 1.6, alpha = 0.4) +
geom_smooth(method = "lm", size = 1.4, alpha = 0.2) +
labs(x = "Offspring Temperature (°C)",
y = "Size (mm)",
colour = "Parental Treatment") +
scale_x_continuous(breaks = c(12,17,22)) +
scale_colour_manual(values = c("Heatwave" = "coral3", "Control" = "skyblue3")) +
theme_matt(base_size = 15) + theme(legend.position = "bottom",
panel.grid = element_blank(),
panel.border = element_rect(fill = NA, colour = "black"))
#car::Anova(f1_size.model, type = "III")
size_temp2 = emmeans::emtrends(F1_size.model, c("Month", "Day", "Parental_treatment"), var = "Offspring_temp") %>%
as.data.frame() %>%
ggplot(aes(x = Parental_treatment, y = Offspring_temp.trend,
colour = Day, group = Day)) +
facet_wrap(Month~.) +
geom_hline(yintercept = 0) +
geom_line(linewidth = 1.5,
position = position_dodge(width = 0.5)) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL),
size = 1.5, width = 0.5,
position = position_dodge(width = 0.5)) +
geom_point(size = 3,
position = position_dodge(width = 0.5)) +
scale_colour_manual(values = day_cols) +
labs(x = "Parental Treatment",
y = "Size Slope (mm / °C)") +
theme_matt(base_size = 15) +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 360-45,
hjust = 0, vjust = 0.5))
ggarrange(size_temp1, size_temp2, labels = "AUTO", common.legend = F, legend = "bottom")

# F0_rf_summary$month = factor(F0_rf_summary$month, levels = c("June", "August", "November"))
# F0_rf_summary$duration = factor(F0_rf_summary$duration, levels = c("short", "long"))
# F0_dur_summary$month = factor(F0_dur_summary$month, levels = c("June", "August", "November"))
#
# param_list = list("colour" = "black",
# "width" = 0.2)
#
# RF_short_db = plot(F0_RF_short,
# axes.title.fontsize = 10,
# tick.fontsize = 10,
# effsize.markersize = 3,
# swarmplot.params = param_list,
# rawplot.ylabel = "Production",
# theme = ggpubr::theme_pubr())
#
# RF_long_db = plot(F0_RF_long,
# axes.title.fontsize = 10,
# tick.fontsize = 10,
# effsize.markersize = 3,
# swarmplot.params = param_list,
# rawplot.ylabel = "Production",
# theme = ggpubr::theme_pubr())
#
# b1 = ggplot() + theme_pubclean() + ggtitle(" Short Heat Waves")
# b2 = ggplot() + theme_pubclean() + ggtitle(" Long Heat Waves")
# F0_fecundity_plot = ggarrange(b1, RF_short_db, b2, RF_long_db, ncol = 1, nrow = 4, heights = c(0.1, 1, 0.1, 1))
# x.axis_labels = c("1" = "short", "2" = "long", "3" = "short", "4" = "long")
#
# F0_grid = F0_rf_summary %>%
# mutate(month = fct_relevel(month, "June", "August", "November")) %>%
# ggplot(aes(x = duration, y = difference, colour = trait, shape = duration)) +
# facet_grid(. ~ month) +
# geom_hline(yintercept = 0, colour = "black", linewidth = 0.3) +
# geom_errorbar(aes(ymin = bca_ci_low, ymax = bca_ci_high), width = 0, linewidth = 1.3) +
# geom_point(size = 5, fill = "white") +
# scale_colour_manual(values = c("body size" = "grey75", "production" = "black")) +
# scale_shape_manual(values = c("long" = 16, "short" = 21)) +
# scale_x_discrete(labels= x.axis_labels) +
# ggtitle("Direct Effects (F0)") +
# xlab("") +
# ylab("Effect Size\nHeatwave - Control") +
# ylim(-1,1.1) +
# theme_bw(base_size = 12) +
# theme(panel.grid = element_blank(),
# axis.text = element_text(colour = "black"),
# axis.text.x = element_text(angle = -45, hjust = 0, vjust = 0.5),
# legend.position = "none")
#
# F1_summary = bind_rows(F1_rf_effect_size, F1_bs_effect_size) %>%
# dplyr::select(variable, difference,
# bca_ci_low, bca_ci_high,
# month, duration, trait, generation, off_temp) %>%
# mutate("order_code" = paste(trait, duration, sep = "_"),
# "order_number" = case_when(
# order_code == "production_short" ~ 1,
# order_code == "production_long" ~ 2,
# order_code == "body size_short" ~ 3,
# order_code == "body size_long" ~ 4),
# month = fct_relevel(month, "June", "August", "November"))
#
# F1_summary$order_number = factor(F1_summary$order_number, levels = c(1,2,3,4))
# F1_grid = ggplot(F1_summary, aes(x = order_number, y = difference, colour = trait, shape = duration, group = trait)) +
# facet_grid(off_temp ~ month, ) +
# geom_hline(yintercept = 0, colour = "black", linewidth = 0.3) +
# geom_line() +
# geom_errorbar(aes(ymin = bca_ci_low, ymax = bca_ci_high), width = 0, linewidth = 1) +
# geom_point(size = 3, fill = "white") +
# scale_colour_manual(values = c("body size" = "grey75", "production" = "black")) +
# scale_shape_manual(values = c("long" = 16, "short" = 21)) +
# xlim(0.5,4.5) +
# scale_x_discrete(labels= x.axis_labels) +
# xlab("") +
# ylab("Effect Size\nHeatwave - Control") +
# ggtitle("Transgenerational Effects (F1)") +
# theme_bw(base_size = 12) +
# theme(panel.grid = element_blank(),
# strip.background.x = element_blank(),
# strip.text.x = element_blank(),
# axis.text = element_text(colour = "black"),
# axis.text.x = element_text(angle = -45, hjust = 0, vjust = 0.5))
#
# ggarrange(F0_grid, F1_grid, nrow = 2, ncol = 1, heights = c(0.45,1), common.legend = T, legend = "right")
Supplemental Information
#field tpc parameters
comb_params %>%
mutate(curve_id = fct_relevel(curve_id, c("January", "February", "March", "April", "May", "June",
"July", "August", "September", "October", "November_1", "November_2"))) %>%
ggplot(aes(x = curve_id, y = estimate, colour = species)) +
facet_wrap(term~metric, scales = 'free_y') +
geom_point(size = 4) +
geom_linerange(aes(ymin = conf_lower, ymax = conf_upper)) +
scale_colour_manual(values = c("royalblue1", "indianred2")) +
labs(x = "Month",
y = "Parameter Estimate",
colour = "Species") +
theme_bw(base_size = 16) +
theme(panel.grid = element_blank(),
axis.text.x = element_text(angle = 315, hjust = 0, vjust = 0.5))

#field tpc parameters
ggplot(comb_params, aes(x = growth_temp, y = estimate, colour = species)) +
facet_wrap(term~metric, scales = 'free_y') +
geom_smooth(data = filter(comb_params, curve_id != "November_2"),
method = "lm", se = F) +
geom_point(size = 4) +
geom_linerange(aes(ymin = conf_lower, ymax = conf_upper)) +
scale_colour_manual(values = c("royalblue1", "indianred2")) +
labs(x = "Collection Temperature (°C)",
y = "Parameter Estimate",
colour = "Species") +
theme_bw(base_size = 12) +
theme(panel.grid = element_blank())

comb_params %>%
filter(term == "topt") %>%
ggplot(aes(x = growth_temp, y = margin, colour = species, group = species)) +
facet_grid(metric~., scales = 'free_y') +
geom_hline(yintercept = 0) +
geom_smooth(data = filter(comb_params, term == "topt" & curve_id != "November_2"),
method = "lm", se = F, linewidth = 1, colour = "black") +
geom_point(size = 4) +
scale_colour_manual(values = c("royalblue1", "indianred2")) +
labs(x = "Collection Temperature",
y = "Safety Margin") +
theme_bw(base_size = 18) +
theme(panel.grid = element_blank())

ggplot(f0_model_data,
aes(x = Day, y = Hatched, colour = Month, group = female_ID)) +
facet_grid(Month~Treatment, scales = "free_y") +
geom_line() +
theme_matt() +
theme(panel.border = element_rect(fill = NA, colour = "black"))

# #Subsequent Rows
# F1_hs_effect_size$trait = "success"
# F1_hs_effect_size$generation = "F1"
#
# F1_total_effect_size$trait = "epr"
# F1_total_effect_size$generation = "F1"
#
# F1_rf_effect_size$trait = "production"
# F1_rf_effect_size$generation = "F1"
#
# F1_bs_effect_size$trait = "body size"
# F1_bs_effect_size$generation = "F1"
#
# F0_data = bind_rows(F0_hs_summary, F0_total_summary,F0_rf_summary, F0_size_summary) %>%
# dplyr::select(trait, difference, bca_ci_low, bca_ci_high, month, duration, trait, generation) %>%
# mutate("order_code" = paste(trait, duration, sep = "_"),
# "order_number" = case_when(
# order_code == "total_short" ~ 1,
# order_code == "total_long" ~ 2,
# order_code == "success_short" ~ 3,
# order_code == "success_long" ~ 4,
# order_code == "production_short" ~ 5,
# order_code == "production_long" ~ 6,
# order_code == "size_long" ~ 7),
# month = fct_relevel(month, "June", "August", "November"),
# trait = fct_relevel(trait, "total", "success", "production", "size"),
# duration = fct_relevel(duration, "short", "long"),
# group_ID = paste(month, trait, sep = "_"))
#
# F0_data$order_number = factor(F0_data$order_number, levels = c(1,2,3,4,5,6,7))
#
#
# F1_data = bind_rows(F1_total_effect_size, F1_hs_effect_size, F1_rf_effect_size, F1_bs_effect_size) %>%
# dplyr::select(trait, difference, bca_ci_low, bca_ci_high, month, duration, generation, off_temp) %>%
# mutate("order_code" = paste(trait, duration, sep = "_"),
# "order_number" = case_when(
# order_code == "epr_short" ~ 1,
# order_code == "epr_long" ~ 2,
# order_code == "success_short" ~ 3,
# order_code == "success_long" ~ 4,
# order_code == "production_short" ~ 5,
# order_code == "production_long" ~ 6,
# order_code == "body size_short" ~ 7,
# order_code == "body size_long" ~ 8),
# trait = if_else(trait == "epr", "total", trait),
# month = fct_relevel(month, "June", "August", "November"),
# trait = fct_relevel(trait, "total", "success", "production", "size"),
# duration = fct_relevel(duration, "short", "long"))
#
#
# F1_data$order_number = factor(F1_data$order_number, levels = c(1,2,3,4,5,6,7,8))
#
# #Top row - F0 (direct effects)
# x.axis_labels = c("1" = "short", "2" = "long", "3" = "short", "4" = "long",
# "5" = "short", "6" = "long", "7" = "short", "8" = "long")
#
# F0_grid = ggplot(F0_data, aes(x = duration, y = difference, colour = trait, group = group_ID)) +
# facet_grid(. ~ month) +
# geom_line(position = position_dodge(width = 0.7),
# linewidth = 1) +
# geom_hline(yintercept = 0, colour = "black", linewidth = 0.3) +
# geom_errorbar(aes(ymin = bca_ci_low, ymax = bca_ci_high), width = 0, linewidth = 1,
# position = position_dodge(width = 0.7)) +
# geom_point(size = 4, fill = "white", position = position_dodge(width = 0.7)) +
# scale_shape_manual(values = c("long" = 16, "short" = 21)) +
# scale_x_discrete(labels= x.axis_labels) +
# scale_colour_manual(values = c("size" = "darkgrey",
# "success" = "gold",
# "production" = "forestgreen",
# "total" = "cornflowerblue")) +
# xlab("") +
# ylab("Effect Size\nHeatwave - Control") +
# theme_bw(base_size = 12) +
# theme(panel.grid = element_blank(),
# axis.text = element_text(colour = "black"),
# axis.text.x = element_text(angle = -45, hjust = 0, vjust = 0.5),
# legend.position = "none")
#
# #Following three rows - F1 (transgeneration / indirect effects)
# F1_grid = ggplot(F1_data, aes(x = duration, y = difference, colour = trait, group = trait)) +
# facet_grid(off_temp ~ month, ) +
# geom_hline(yintercept = 0, colour = "black", linewidth = 0.3) +
# geom_line(position = position_dodge(width = 0.5),
# linewidth = 1) +
# geom_errorbar(aes(ymin = bca_ci_low, ymax = bca_ci_high), width = 0, linewidth = 1,
# position = position_dodge(width = 0.5)) +
# geom_point(size = 3, fill = "white", position = position_dodge(width = 0.5)) +
# scale_shape_manual(values = c("long" = 16, "short" = 21)) +
# xlim(0.5,4.5) +
# scale_x_discrete(labels= x.axis_labels) +
# scale_colour_manual(values = c("size" = "darkgrey",
# "success" = "gold",
# "production" = "forestgreen",
# "total" = "cornflowerblue")) +
# xlab("") +
# ylab("Effect Size \nHeatwave - Control") +
# theme_bw(base_size = 12) +
# theme(panel.grid = element_blank(),
# strip.background.x = element_blank(),
# strip.text.x = element_blank(),
# axis.text = element_text(colour = "black"),
# axis.text.x = element_text(angle = -45, hjust = 0, vjust = 0.5))
#
# ggarrange(F0_grid, F1_grid, nrow = 2, ncol = 1, heights = c(0.35,1), common.legend = T, legend = "right", labels = "AUTO")
# effect_corr = F1_summary %>%
# select(trait, difference, month, duration, off_temp) %>%
# pivot_wider(id_cols = c(month, duration, off_temp),
# names_from = trait,
# values_from = difference)
#
# ggplot(effect_corr, aes(x = `body size`, y = production)) +
# geom_hline(yintercept = 0) +
# geom_vline(xintercept = 0) +
# geom_point(size = 3) +
# geom_smooth(method = "lm", se = F,
# colour = "grey60",
# size = 1) +
# labs(x = "Body Size Effect",
# y = "Production Effect") +
# theme_matt()
#Effect of heatwave duration WITHIN treatment
f0_model_data %>%
group_by(Treatment, Day, Month) %>%
summarise(mean_hatched = mean(Hatched, na.rm = T)) %>%
ungroup() %>%
pivot_wider(id_cols = c("Treatment", "Month"),
values_from = mean_hatched,
names_from = Day) %>%
mutate("effect" = `5_to_7` - `1_to_3`)
## # A tibble: 6 × 5
## Treatment Month `1_to_3` `5_to_7` effect
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Control August 125. 67.2 -57.3
## 2 Control June 104. 84.4 -19.6
## 3 Control November 0.871 7.26 6.39
## 4 Heatwave August 170. 84.4 -85.4
## 5 Heatwave June 108. 58.3 -49.6
## 6 Heatwave November 2.2 11.5 9.26
duration.model = lme4::lmer(data = f0_model_data,
Hatched ~ Treatment * Day * Month + (1|Month))
duration_pairs = emmeans::emmeans(duration.model,
~ Day | Month * Treatment) %>%
pairs()
as.data.frame(summary(duration_pairs))[c('Month',
'Treatment',
'contrast',
'estimate',
'SE')] %>%
mutate(Month = fct_relevel(Month, c("June", "August", "November")),
estimate = estimate * -1) %>% #Flips sign to make contrast Long - Short
ggplot(aes(x = Month, fill = Treatment, y = estimate)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.9), colour = "black", size = 1) +
geom_errorbar(aes(ymin = estimate - SE, ymax = estimate + SE),
width = 0.1, size = 1,
position = position_dodge(width = 0.9)) +
geom_hline(yintercept = 0) +
scale_fill_manual(values = c("grey30", "white")) +
labs(x = "",
y = "Duration Contrast \n Long - Short events") +
theme_pubr(base_size = 18)

# How does heat wave duration affect transgenerational effects? Reaction norms shown below for effect size comparisons (heatwave - control) for different duration of parental exposure
# F1_data %>%
# dplyr::select(month, duration, off_temp, trait, difference, bca_ci_low, bca_ci_high) %>%
# mutate("ID" = paste(month, off_temp, trait, sep = "_"),
# month = fct_relevel(month, c("June", "August", "November")),
# duration = fct_relevel(duration, c("short", "long"))) %>%
# ggplot(aes(x = duration, y = difference, colour = month, group = ID)) +
# facet_wrap(trait~.) +
# geom_hline(yintercept = 0) +
# geom_line(size = 1, position = position_dodge(width = 0.1)) +
# geom_point(size = 2, position = position_dodge(width = 0.1)) +
# geom_errorbar(aes(ymin = bca_ci_low, ymax = bca_ci_high),
# size = 0.75, width = 0.1,
# position = position_dodge(width = 0.1)) +
# labs(x = "Parental Exposure Duration",
# y = "Effect Size (Hedge's g) \n Heatwave - Control") +
# ylim(-5,5) +
# theme_pubr(base_size = 18)
# #Pulls out reaction norms where there's a sign change (changes from positive, neutral, or negative between duration groups)
#
# duration_effects = F1_data %>%
# dplyr::select(month, duration, off_temp, trait, difference, bca_ci_low, bca_ci_high) %>%
# mutate("ID" = paste(month, off_temp, trait, sep = "_"),
# month = fct_relevel(month, c("June", "August", "November")),
# duration = fct_relevel(duration, c("short", "long"))) %>%
# group_by(ID, trait, duration) %>%
# mutate("zero_diff" = case_when(
# sign(bca_ci_low) == sign(bca_ci_high) ~ "does not overlap zero",
# sign(bca_ci_low) != sign(bca_ci_high) ~ "overlaps zero"
# )) %>%
# ungroup(duration) %>%
# mutate("change" = case_when(
# sign(difference)[1] == sign(difference)[2] & zero_diff[1] == zero_diff[2] ~ "Same",
# sign(difference)[1] != sign(difference)[2] & zero_diff[1] == "overlaps zero" & zero_diff[2] == "overlaps zero" ~ "Same",
# sign(difference)[1] != sign(difference)[2] & zero_diff[1] == zero_diff[2] & zero_diff[1] == "does not overlap zero" ~ "Different",
# sign(difference)[1] != sign(difference)[2] & zero_diff[1] != zero_diff[2] ~ "Different",
# sign(difference)[1] == sign(difference)[2] & zero_diff[1] != zero_diff[2] ~ "Different"
# )) %>%
# arrange(month, off_temp, trait) %>%
# filter(change == "Different")
#
# select_rnorms = duration_effects %>%
# dplyr::select(-duration, -difference, -bca_ci_low, -bca_ci_high, -ID, -zero_diff) %>%
# distinct()
#
# sig_changes = F1_data %>%
# dplyr::select(month, duration, off_temp, trait, difference, bca_ci_low, bca_ci_high) %>%
# mutate("ID" = paste(month, off_temp, trait, sep = "_"),
# month = fct_relevel(month, c("June", "August", "November")),
# duration = fct_relevel(duration, c("short", "long"))) %>%
# filter(ID %in% select_rnorms$ID)
#
# ggplot(sig_changes, aes(x = duration, y = difference, colour = month, group = ID)) +
# facet_wrap(trait~.) +
# geom_hline(yintercept = 0) +
# geom_line(size = 1, position = position_dodge(width = 0.1)) +
# geom_point(size = 2, position = position_dodge(width = 0.1)) +
# geom_errorbar(aes(ymin = bca_ci_low, ymax = bca_ci_high),
# size = 0.75, width = 0.1,
# position = position_dodge(width = 0.1)) +
# geom_label_repel(data = sig_changes %>% filter(duration == "long"),
# aes(label = off_temp,
# x = duration,
# y = difference,
# color = month),
# box.padding = 0.5,
# nudge_x = 0.2,
# size = 7,
# show.legend=FALSE) +
# labs(x = "Parental Exposure Duration",
# y = "Effect Size (Hedge's g)\nHeatwave - Control") +
# ylim(-5,5) +
# theme_pubr(base_size = 18)
---
title: "Figures for Seasonally variable thermal performance curves prevent adverse effects of heatwaves"
output: 
  html_document:
          code_folding: hide
          code_download: true
          toc: true
          toc_float: true
  github_document:
          toc: true
          toc_depth: 2
          html_preview: false
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE, fig.align="center", message = F, warning = F}
knitr::opts_chunk$set(
  echo = knitr::is_html_output(),
  fig.align = "left",
  fig.path = "../Figures/markdown/",
  message = FALSE,
  warning = FALSE,
  collapse = T,
  dev = c("png", "pdf")
)

st.err <- function(x, na.rm=FALSE) {
  if(na.rm==TRUE) x <- na.omit(x)
  sd(x)/sqrt(length(x))
}

theme_matt = function(base_size = 18,
                      dark_text = "grey20"){
  mid_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[2]
  light_text <-  monochromeR::generate_palette(dark_text, "go_lighter", n_colours = 5)[3]
  
  ggpubr::theme_pubr(base_family="sans") %+replace% 
    theme(
      panel.background  = element_rect(fill="transparent", colour=NA), 
      plot.background = element_rect(fill="transparent", colour=NA), 
      legend.background = element_rect(fill="transparent", colour=NA),
      legend.key = element_rect(fill="transparent", colour=NA),
      text = element_text(colour = mid_text, lineheight = 1.1),
      title = element_text(size = base_size * 1.5,
                           colour = dark_text),
      axis.text = element_text(size = base_size,
                               colour = mid_text),
      axis.title = element_text(size = base_size * 1.2,
                                margin = margin(0, 8, 0, 0)),
      legend.text = element_text(size=base_size * 0.9),
      legend.title = element_text(size = base_size * 0.9, 
                                  face = "bold"),
      plot.margin = margin(0.25, 0.25, 0.25, 0.25,"cm")
    )
}

h1_epr$Month = factor(h1_epr$Month, levels = c("July", "August", "September", "October", "November_1", "November_2"))

huds_h1_epr$Month = factor(huds_h1_epr$Month, levels = c("January", "February", "March", "April", "May", "June"))

tonsa_colors = c("July" = "#791101", "August" = "#AD2106", "September" = "#CC3403", 
                 "October" = "#EF5F08", "November_1" = "#F27006", "November_2" = "#EEB60A")

huds_colors = c("January" = "#061B24", "February" = "#0F4256", "March" = "#156D8D", 
                "April" = "#2FAADC", "May" = "#61BFE3", "June" = "#97D5EE")

comb_colors = c(huds_colors, tonsa_colors)

a = ggplot() + theme_pubclean()

surv$Month = factor(surv$Month, levels = c("July", "August", "September", "October", "November_1", "November_2"))
huds_surv$Month = factor(huds_surv$Month, levels = c("January", "February", "March", "April", "May", "June"))

comb_preds$curve_id = factor(comb_preds$curve_id, 
                             levels = c("January", "February", "March", "April", "May", "June",
                                        "July", "August", "September", "October", "November_1", "November_2"))

comb_d$curve_id = factor(comb_d$curve_id, 
                         levels = c("January", "February", "March", "April", "May", "June",
                                    "July", "August", "September", "October", "November_1", "November_2"))

comb_surv$Month = factor(comb_surv$Month, 
                         levels = c("January", "February", "March", "April", "May", "June",
                                    "July", "August", "September", "October", "November_1", "November_2"))

```

## Main Text Figures
```{r figure-2-combined-tpcs, fig.height=6}
comb_epr_plot = comb_preds %>% 
  filter(metric == "EPR") %>% 
  ggplot() +
  geom_point(aes(temp, rate, colour = curve_id), 
             filter(comb_d, metric == "EPR"), 
             size = 1.5, alpha = 0.6, 
             position = position_jitter(width = 0.5, height = 0)) +
  geom_ribbon(aes(temp, ymin = conf_lower, ymax = conf_upper, group = curve_id), 
              filter(comb_boot_conf_preds, metric == "EPR"), 
              fill = 'grey60', alpha = 0.3) +
  geom_line(aes(temp, .fitted, col = curve_id), linewidth = 2) +
  scale_colour_manual(values = comb_colors) + 
  labs(x = "", 
       y = "Egg Production \n(eggs/female/day)",
       colour = "Month") + 
  theme_matt(base_size = 12)

comb_hs_plot = comb_preds %>% 
  filter(metric == "HF") %>% 
  ggplot() +
  geom_point(aes(temp, rate, colour = curve_id), filter(comb_d, metric == "HF"), size = 1.5, alpha = 0.6, 
             position = position_jitter(width = 0.5, height = 0)) +
  geom_ribbon(aes(temp, ymin = conf_lower, ymax = conf_upper, group = curve_id), filter(comb_boot_conf_preds, metric == "HF"), fill = 'grey60', alpha = 0.3) +
  geom_line(aes(temp, .fitted, col = curve_id), linewidth = 2) +
  #scale_color_brewer(type = "div", palette = 5, direction = -1) + 
  #scale_color_viridis_d(option = "mako") + 
  scale_colour_manual(values = comb_colors) + 
  labs(x = "", 
       y = "Hatching Success \n(%)",
       colour = "Month") + 
  theme_matt(base_size = 12)

comb_rf_plot = comb_preds %>% 
  filter(metric == "RF") %>% 
  ggplot() +
  geom_point(aes(temp, rate, colour = curve_id), filter(comb_d, metric == "RF"), size = 1.5, alpha = 0.6, 
             position = position_jitter(width = 0.5, height = 0)) +
  geom_ribbon(aes(temp, ymin = conf_lower, ymax = conf_upper, group = curve_id), filter(comb_boot_conf_preds, metric == "RF"), fill = 'grey60', alpha = 0.3) +
  geom_line(aes(temp, .fitted, col = curve_id), linewidth = 2) +
  #scale_color_brewer(type = "div", palette = 5, direction = -1) + 
  #scale_color_viridis_d(option = "mako") + 
  scale_colour_manual(values = comb_colors) + 
  labs(x = "Temperature (°C)", 
       y = "Offspring Production \n(offspring/female/day)",
       colour = "Month") + 
  theme_matt(base_size = 12)

comb_tsc = ggplot(comb_surv, aes(x=Temp, y=Surv, colour=Month)) + 
  geom_point(size=1.5, position=position_jitter(width=0.1, height=0.03)) +
  xlab("Temperature (°C)")+
  ylab("Survivorship \n(proportion survived)")+
  labs(colour = "Month") + 
  geom_hline(yintercept = 0.5, linetype = "dashed") +
  geom_smooth(method = "glm", method.args = list(family = "binomial"), se=T, linewidth = 2) +
  scale_y_continuous(breaks = c(0,1)) + 
  #scale_color_brewer(type = "div", palette = 5, direction = -1) + 
  #scale_color_viridis_d(option = "mako") + 
  scale_colour_manual(values = comb_colors) + 
  theme_matt(base_size = 12)

ggarrange(comb_epr_plot, comb_hs_plot, comb_rf_plot, comb_tsc, 
          ncol = 2, nrow = 2,
          common.legend = T, legend = "bottom", 
          labels = "AUTO", vjust = 1)

# ggarrange(comb_epr_plot, comb_hs_plot, comb_rf_plot, comb_tsc, nrow = 1,
#           common.legend = T, legend = "bottom")
```

```{r figure-3-curve-parameters, warning = F, message = F, fig.width=8, fig.height=7}
combined_opt_coll = comb_params %>% 
  filter(metric == "RF" & term == "topt") %>% 
  ggplot(aes(x = growth_temp, y = estimate, shape = species)) + 
  geom_smooth(data = filter(comb_params, metric == "RF" & term == "topt" & curve_id != "November_2"),
              method = "lm", colour = "grey50") + 
  geom_point(size = 3, stroke = 1) + 
  scale_shape_manual(values = c(16,21)) + 
  ylab("Optimum (°C)") + 
  xlab("Collection Temperature (°C)") + 
  labs(colour = "Month") + 
  theme_matt(base_size = 12)

combined_opt_diff = comb_params %>% 
  filter(metric == "RF" & term == "topt") %>% 
  ggplot(aes(x = growth_temp, y = margin, shape = species)) +
  geom_hline(yintercept = 0, linewidth =1, linetype = "dashed") +
  geom_smooth(data = filter(comb_params, metric == "RF" & term == "topt" & curve_id != "November_2"),
              method = "lm", colour = "grey50") + 
  geom_point(size = 3, stroke = 1) + 
  ylab("Margin (°C)") + 
  xlab("Collection Temperature (°C)") + 
  scale_shape_manual(values = c(16,21)) + 
  theme_matt(base_size = 12) 

combined_ld_coll = ggplot(combined_tolerance, aes(x = Coll_temp, y = LD50, shape = species)) + 
  geom_smooth(method = "lm", colour = "grey50") + 
  geom_point(size = 3, stroke = 1) + 
  scale_shape_manual(values = c(16,21)) + 
  ylab("Thermal Tolerance (°C)") + 
  xlab("Collection Temperature (°C)") + 
  theme_matt(base_size = 12)

combined_ld_diff = ggplot(combined_tolerance, aes(x = Coll_temp, y = margin, shape = species)) +
  geom_smooth(method = "lm", colour = "grey50") + 
  geom_point(size = 3, stroke = 1) + 
  scale_shape_manual(values = c(16,21)) + 
  ylab("Warming Tolerance (°C)") + 
  xlab("Collection Temperature (°C)") + 
  theme_matt(base_size = 12)

ggarrange(combined_opt_coll, combined_opt_diff, combined_ld_coll, combined_ld_diff, ncol = 2, nrow = 2, common.legend = T,
          legend = "bottom", labels = "AUTO")
```

```{r}
F0_hatched.model = lme4::glmer(data = f0_model_data, family = poisson, 
                               Hatched ~ Treatment * Day + 
                                 (1|Month) + (1|female_ID))

summary(F0_hatched.model)
car::Anova(F0_hatched.model, type = "III")
```

```{r f0-hatched, fig.width=7, fig.height=6}
f0_model_females = F0_epr %>% 
  group_by(Month, Treatment, Female) %>% 
  count() %>% 
  filter(n == 2) %>% 
  mutate('female_ID' = paste(Month, Treatment, Female, sep = "_"))

f0_model_data = F0_epr %>% 
  mutate('female_ID' = paste(Month, Treatment, Female, sep = "_")) %>% 
  filter(female_ID %in% f0_model_females$female_ID)

f0_summary = f0_model_data %>% 
  group_by(Month, Treatment, Day) %>%  
  summarise("mean_hatched" = mean(Hatched, na.rm = T),
            "hatched_se" = st.err(Hatched, na.rm = T)) %>% 
  ungroup() %>% 
  mutate(Day = if_else(Day == "1_to_3", "Short", "Long"),
         Month = fct_relevel(Month, "June", "August", "November"),
         Day = fct_relevel(Day, "Short", "Long"))

ggplot(f0_summary, 
       aes(x = Month, y = mean_hatched, colour = Treatment)) + 
  facet_grid(.~Day, scales = "free_y") + 
  geom_hline(yintercept = 0) + 
  geom_errorbar(aes(ymin = mean_hatched - hatched_se, 
                    ymax = mean_hatched + hatched_se),
                width = 0.2, linewidth = 1,
                position = position_dodge(width = 0.3)) + 
  geom_point(size = 4, position = position_dodge(width = 0.3)) + 
  scale_colour_manual(values = c("Heatwave" = "coral3", "Control" = "skyblue3")) + 
  labs(x = "Month", 
       y = "Mean Offspring Production \n(offspring/female/day)") + 
  theme_matt() + 
  theme(panel.border = element_rect(fill = NA, colour = "black"),
        legend.position = "bottom",
        axis.text.x = element_text(angle = 300, hjust = 0, vjust = 0.5))
```

```{r f1-hatched, fig.width=8, fig.height=6}
f1_model_data = F1_epr %>% 
  mutate(Offspring_temp = as.factor(Offspring_temp)) %>% 
  ungroup() %>% 
  mutate(Day = if_else(Day == "1_to_3", "Short", "Long"),
         Month = fct_relevel(Month, "June", "August", "November"),
         Day = fct_relevel(Day, "Short", "Long"))

ggplot(f1_model_data, 
       aes(x = Offspring_temp, y = Hatched, colour = Parental_treatment)) + 
  facet_grid(Month~Day) + 
  #geom_violin(position = position_dodge(width = 1)) + 
  geom_boxplot(position = position_dodge(width = 0.5),
               width = 0.3) + 
  geom_point(position = position_dodge(width = 0.5),
             alpha = 0.5) + 
  scale_colour_manual(values = c("Heatwave" = "coral3", "Control" = "skyblue3")) + 
  scale_y_continuous(breaks = c(0, 100, 200)) + 
  labs(x = "Offspring Temp. (°C)", 
       y = "Offspring Production \n(offspring/female/day)",
       colour = "Parental Treatment") + 
  theme_matt() + 
  theme(panel.border = element_rect(fill = NA, colour = "black"),
        legend.position = "bottom")
```

```{r}
F1_hatched.model = lme4::glmer(data = f1_model_data, family = poisson, 
                               Hatched ~ Parental_treatment * Day * Offspring_temp + 
                                 (1 + Parental_treatment|Month))

summary(F1_hatched.model)
coefficients(F1_hatched.model)
car::Anova(F1_hatched.model, type = "III")
```

```{r}
f1_size_data = F1_fbs %>% 
  ungroup() %>% 
  mutate(Day = if_else(Day == "1_to_3", "Short", "Long"),
         Month = fct_relevel(Month, "June", "August", "November"),
         Day = fct_relevel(Day, "Short", "Long"))

F1_size.model = lme4::lmer(data = f1_size_data, 
                           Size ~ Parental_treatment * Offspring_temp * Day * Month + 
                             (1|Month))

summary(F1_size.model)
coefficients(F1_size.model)
car::Anova(F1_size.model, type = "III")
```

```{r f1-size, fig.width = 10, fig.height=6}
day_cols = c("Short" = "grey80", "Long" = "grey30")

size_temp1 = ggplot(f1_size_data, aes(x = Offspring_temp, y = Size, colour = Parental_treatment)) + 
  facet_grid(Month~Day) + 
  geom_jitter(width = 0.5, size = 1.6, alpha = 0.4) +
  geom_smooth(method = "lm", size = 1.4, alpha = 0.2) + 
  labs(x = "Offspring Temperature (°C)",
       y = "Size (mm)",
       colour = "Parental Treatment") + 
  scale_x_continuous(breaks = c(12,17,22)) + 
  scale_colour_manual(values = c("Heatwave" = "coral3", "Control" = "skyblue3")) + 
  theme_matt(base_size = 15) + theme(legend.position = "bottom",
                                     panel.grid = element_blank(),
                                     panel.border = element_rect(fill = NA, colour = "black"))

#car::Anova(f1_size.model, type = "III")
size_temp2 = emmeans::emtrends(F1_size.model, c("Month", "Day", "Parental_treatment"), var = "Offspring_temp") %>% 
  as.data.frame() %>% 
  ggplot(aes(x = Parental_treatment, y = Offspring_temp.trend, 
             colour = Day, group = Day)) + 
  facet_wrap(Month~.) + 
  geom_hline(yintercept = 0) + 
  geom_line(linewidth = 1.5,
            position = position_dodge(width = 0.5)) + 
  geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL),
                size = 1.5, width = 0.5,
                position = position_dodge(width = 0.5)) + 
  geom_point(size = 3,
             position = position_dodge(width = 0.5)) + 
  scale_colour_manual(values = day_cols) + 
  labs(x = "Parental Treatment",  
       y = "Size Slope (mm / °C)") + 
  theme_matt(base_size = 15) + 
  theme(legend.position = "bottom",
        axis.text.x = element_text(angle = 360-45,
                                   hjust = 0, vjust = 0.5))

ggarrange(size_temp1, size_temp2, labels = "AUTO", common.legend = F, legend = "bottom")
```


```{r}
# F0_rf_summary$month = factor(F0_rf_summary$month, levels = c("June", "August", "November"))
# F0_rf_summary$duration = factor(F0_rf_summary$duration, levels = c("short", "long"))
# F0_dur_summary$month = factor(F0_dur_summary$month, levels = c("June", "August", "November"))
# 
# param_list = list("colour" = "black",
#                   "width" = 0.2) 
# 
# RF_short_db = plot(F0_RF_short, 
#                    axes.title.fontsize = 10,
#                    tick.fontsize = 10,
#                    effsize.markersize = 3,
#                    swarmplot.params = param_list,
#                    rawplot.ylabel = "Production",
#                    theme = ggpubr::theme_pubr())
# 
# RF_long_db = plot(F0_RF_long, 
#                   axes.title.fontsize = 10,
#                   tick.fontsize = 10,
#                   effsize.markersize = 3,
#                   swarmplot.params = param_list,
#                   rawplot.ylabel = "Production",
#                   theme = ggpubr::theme_pubr())
# 
# b1 = ggplot() + theme_pubclean() + ggtitle("          Short Heat Waves")
# b2 = ggplot() + theme_pubclean() + ggtitle("          Long Heat Waves")
# F0_fecundity_plot = ggarrange(b1, RF_short_db, b2, RF_long_db, ncol = 1, nrow = 4, heights = c(0.1, 1, 0.1, 1))
```

```{r figure-4-sim-heatwave-effects, fig.height=6, fig.width=5}
# x.axis_labels = c("1" = "short", "2" = "long", "3" = "short", "4" = "long")
# 
# F0_grid = F0_rf_summary %>% 
#   mutate(month = fct_relevel(month, "June", "August", "November")) %>% 
#   ggplot(aes(x = duration, y = difference, colour = trait, shape = duration)) + 
#   facet_grid(. ~ month) + 
#   geom_hline(yintercept = 0, colour = "black", linewidth = 0.3) +
#   geom_errorbar(aes(ymin = bca_ci_low, ymax = bca_ci_high), width = 0, linewidth = 1.3) + 
#   geom_point(size = 5, fill = "white") + 
#   scale_colour_manual(values = c("body size" = "grey75", "production" = "black")) + 
#   scale_shape_manual(values = c("long" = 16, "short" = 21)) + 
#   scale_x_discrete(labels= x.axis_labels) +
#   ggtitle("Direct Effects (F0)") + 
#   xlab("") +
#   ylab("Effect Size\nHeatwave - Control") + 
#   ylim(-1,1.1) + 
#   theme_bw(base_size = 12) + 
#   theme(panel.grid = element_blank(),
#         axis.text = element_text(colour = "black"),
#         axis.text.x = element_text(angle = -45, hjust = 0, vjust = 0.5),
#         legend.position = "none")
# 
# F1_summary = bind_rows(F1_rf_effect_size, F1_bs_effect_size) %>% 
#   dplyr::select(variable, difference, 
#                 bca_ci_low, bca_ci_high, 
#                 month, duration, trait, generation, off_temp) %>% 
#   mutate("order_code" = paste(trait, duration, sep = "_"),
#          "order_number" = case_when(
#            order_code == "production_short" ~ 1,
#            order_code == "production_long" ~ 2,
#            order_code == "body size_short" ~ 3,
#            order_code == "body size_long" ~ 4),
#          month = fct_relevel(month, "June", "August", "November"))
# 
# F1_summary$order_number = factor(F1_summary$order_number, levels = c(1,2,3,4))
# F1_grid = ggplot(F1_summary, aes(x = order_number, y = difference, colour = trait, shape = duration, group = trait)) +
#   facet_grid(off_temp ~ month, ) + 
#   geom_hline(yintercept = 0, colour = "black", linewidth = 0.3) +
#   geom_line() + 
#   geom_errorbar(aes(ymin = bca_ci_low, ymax = bca_ci_high), width = 0, linewidth = 1) + 
#   geom_point(size = 3, fill = "white") + 
#   scale_colour_manual(values = c("body size" = "grey75", "production" = "black")) + 
#   scale_shape_manual(values = c("long" = 16, "short" = 21)) + 
#   xlim(0.5,4.5) + 
#   scale_x_discrete(labels= x.axis_labels) +
#   xlab("") +
#   ylab("Effect Size\nHeatwave - Control") + 
#   ggtitle("Transgenerational Effects (F1)") + 
#   theme_bw(base_size = 12) + 
#   theme(panel.grid = element_blank(),
#         strip.background.x = element_blank(),
#         strip.text.x = element_blank(),
#         axis.text = element_text(colour = "black"),
#         axis.text.x = element_text(angle = -45, hjust = 0, vjust = 0.5))
# 
# ggarrange(F0_grid, F1_grid, nrow = 2, ncol = 1, heights = c(0.45,1), common.legend = T, legend = "right")
```

## Supplemental Information   
```{r parameters-month, fig.height=9, fig.width=11}
#field tpc parameters
comb_params %>%  
  mutate(curve_id = fct_relevel(curve_id, c("January", "February", "March", "April", "May", "June", 
                                            "July", "August", "September", "October", "November_1", "November_2"))) %>% 
  ggplot(aes(x = curve_id, y = estimate, colour = species)) +
  facet_wrap(term~metric, scales = 'free_y') + 
  geom_point(size = 4) +
  geom_linerange(aes(ymin = conf_lower, ymax = conf_upper)) +
  scale_colour_manual(values = c("royalblue1", "indianred2")) + 
  labs(x = "Month",
       y = "Parameter Estimate",
       colour = "Species") + 
  theme_bw(base_size = 16) +
  theme(panel.grid = element_blank(),
        axis.text.x = element_text(angle = 315, hjust = 0, vjust = 0.5))
```

```{r parameters-coll-temp, fig.height=7, fig.width=9}
#field tpc parameters
ggplot(comb_params, aes(x = growth_temp, y = estimate, colour = species)) +
  facet_wrap(term~metric, scales = 'free_y') + 
  geom_smooth(data = filter(comb_params, curve_id != "November_2"),
              method = "lm", se = F) + 
  geom_point(size = 4) +
  geom_linerange(aes(ymin = conf_lower, ymax = conf_upper)) +
  scale_colour_manual(values = c("royalblue1", "indianred2")) + 
  labs(x = "Collection Temperature (°C)",
       y = "Parameter Estimate",
       colour = "Species") + 
  theme_bw(base_size = 12) +
  theme(panel.grid = element_blank())
```

```{r margins-coll-temp, fig.height=9, fig.width=7}
comb_params %>% 
  filter(term == "topt") %>% 
  ggplot(aes(x = growth_temp, y = margin, colour = species, group = species)) + 
  facet_grid(metric~., scales = 'free_y') + 
  geom_hline(yintercept = 0) +
  geom_smooth(data = filter(comb_params, term == "topt" & curve_id != "November_2"),
              method = "lm", se = F, linewidth = 1, colour = "black") + 
  geom_point(size = 4) +
  scale_colour_manual(values = c("royalblue1", "indianred2")) + 
  labs(x = "Collection Temperature",
       y = "Safety Margin") + 
  theme_bw(base_size = 18) +
  theme(panel.grid = element_blank())
```

```{r}
ggplot(f0_model_data, 
       aes(x = Day, y = Hatched, colour = Month, group = female_ID)) + 
  facet_grid(Month~Treatment, scales = "free_y") + 
  geom_line() + 
  theme_matt() + 
  theme(panel.border = element_rect(fill = NA, colour = "black"))
```


```{r effect-size-grid, fig.height=8, fig.width=8}
# #Subsequent Rows
# F1_hs_effect_size$trait = "success"
# F1_hs_effect_size$generation = "F1"
# 
# F1_total_effect_size$trait = "epr"
# F1_total_effect_size$generation = "F1"
# 
# F1_rf_effect_size$trait = "production"
# F1_rf_effect_size$generation = "F1"
# 
# F1_bs_effect_size$trait = "body size"
# F1_bs_effect_size$generation = "F1"
# 
# F0_data = bind_rows(F0_hs_summary, F0_total_summary,F0_rf_summary, F0_size_summary) %>% 
#   dplyr::select(trait, difference, bca_ci_low, bca_ci_high, month, duration, trait, generation) %>% 
#   mutate("order_code" = paste(trait, duration, sep = "_"),
#          "order_number" = case_when(
#            order_code == "total_short" ~ 1,
#            order_code == "total_long" ~ 2,
#            order_code == "success_short" ~ 3,
#            order_code == "success_long" ~ 4,
#            order_code == "production_short" ~ 5,
#            order_code == "production_long" ~ 6,
#            order_code == "size_long" ~ 7),
#          month = fct_relevel(month, "June", "August", "November"),
#          trait = fct_relevel(trait, "total", "success", "production", "size"),
#          duration = fct_relevel(duration, "short", "long"),
#          group_ID = paste(month, trait, sep = "_"))
# 
# F0_data$order_number = factor(F0_data$order_number, levels = c(1,2,3,4,5,6,7))
# 
# 
# F1_data = bind_rows(F1_total_effect_size, F1_hs_effect_size, F1_rf_effect_size, F1_bs_effect_size) %>% 
#   dplyr::select(trait, difference, bca_ci_low, bca_ci_high, month, duration, generation, off_temp) %>% 
#   mutate("order_code" = paste(trait, duration, sep = "_"),
#          "order_number" = case_when(
#            order_code == "epr_short" ~ 1,
#            order_code == "epr_long" ~ 2,
#            order_code == "success_short" ~ 3,
#            order_code == "success_long" ~ 4,
#            order_code == "production_short" ~ 5,
#            order_code == "production_long" ~ 6,
#            order_code == "body size_short" ~ 7,
#            order_code == "body size_long" ~ 8),
#          trait = if_else(trait == "epr", "total", trait),
#          month = fct_relevel(month, "June", "August", "November"),
#          trait = fct_relevel(trait, "total", "success", "production", "size"),
#          duration = fct_relevel(duration, "short", "long"))
# 
# 
# F1_data$order_number = factor(F1_data$order_number, levels = c(1,2,3,4,5,6,7,8))
# 
# #Top row - F0 (direct effects)
# x.axis_labels = c("1" = "short", "2" = "long", "3" = "short", "4" = "long", 
#                   "5" = "short", "6" = "long", "7" = "short", "8" = "long")
# 
# F0_grid = ggplot(F0_data, aes(x = duration, y = difference, colour = trait, group = group_ID)) + 
#   facet_grid(. ~ month) + 
#   geom_line(position = position_dodge(width = 0.7),
#             linewidth = 1) + 
#   geom_hline(yintercept = 0, colour = "black", linewidth = 0.3) +
#   geom_errorbar(aes(ymin = bca_ci_low, ymax = bca_ci_high), width = 0, linewidth = 1, 
#                 position = position_dodge(width = 0.7)) + 
#   geom_point(size = 4, fill = "white", position = position_dodge(width = 0.7)) + 
#   scale_shape_manual(values = c("long" = 16, "short" = 21)) + 
#   scale_x_discrete(labels= x.axis_labels) +
#   scale_colour_manual(values = c("size" = "darkgrey",
#                                  "success" = "gold",
#                                  "production" = "forestgreen",
#                                  "total" = "cornflowerblue")) +  
#   xlab("") +
#   ylab("Effect Size\nHeatwave - Control") + 
#   theme_bw(base_size = 12) + 
#   theme(panel.grid = element_blank(),
#         axis.text = element_text(colour = "black"),
#         axis.text.x = element_text(angle = -45, hjust = 0, vjust = 0.5),
#         legend.position = "none")
# 
# #Following three rows - F1 (transgeneration / indirect effects)
# F1_grid = ggplot(F1_data, aes(x = duration, y = difference, colour = trait, group = trait)) + 
#   facet_grid(off_temp ~ month, ) + 
#   geom_hline(yintercept = 0, colour = "black", linewidth = 0.3) +
#   geom_line(position = position_dodge(width = 0.5),
#             linewidth = 1) + 
#   geom_errorbar(aes(ymin = bca_ci_low, ymax = bca_ci_high), width = 0, linewidth = 1,
#                 position = position_dodge(width = 0.5)) + 
#   geom_point(size = 3, fill = "white", position = position_dodge(width = 0.5)) + 
#   scale_shape_manual(values = c("long" = 16, "short" = 21)) + 
#   xlim(0.5,4.5) + 
#   scale_x_discrete(labels= x.axis_labels) +
#   scale_colour_manual(values = c("size" = "darkgrey",
#                                  "success" = "gold",
#                                  "production" = "forestgreen",
#                                  "total" = "cornflowerblue")) + 
#   xlab("") +
#   ylab("Effect Size \nHeatwave - Control") + 
#   theme_bw(base_size = 12) + 
#   theme(panel.grid = element_blank(),
#         strip.background.x = element_blank(),
#         strip.text.x = element_blank(),
#         axis.text = element_text(colour = "black"),
#         axis.text.x = element_text(angle = -45, hjust = 0, vjust = 0.5))
# 
# ggarrange(F0_grid, F1_grid, nrow = 2, ncol = 1, heights = c(0.35,1), common.legend = T, legend = "right", labels = "AUTO")
```

```{r production-size-change}
# effect_corr = F1_summary %>% 
#   select(trait, difference, month, duration, off_temp) %>%  
#   pivot_wider(id_cols = c(month, duration, off_temp),
#               names_from = trait, 
#               values_from = difference)
# 
# ggplot(effect_corr, aes(x = `body size`, y = production)) + 
#   geom_hline(yintercept = 0) + 
#   geom_vline(xintercept = 0) + 
#   geom_point(size = 3) + 
#   geom_smooth(method = "lm", se = F,
#               colour = "grey60",
#               size = 1) + 
#   labs(x = "Body Size Effect",
#        y = "Production Effect") + 
#   theme_matt()
```


```{r F0-production, fig.height=11, fig.width=8, include = F}
#F0_fecundity_plot
```

```{r F0-duration-effects, fig.width=8, fig.height=8}
#Effect of heatwave duration WITHIN treatment

f0_model_data %>%  
  group_by(Treatment, Day, Month) %>% 
  summarise(mean_hatched = mean(Hatched, na.rm = T)) %>% 
  ungroup() %>% 
  pivot_wider(id_cols = c("Treatment", "Month"),
              values_from = mean_hatched, 
              names_from = Day) %>% 
  mutate("effect" = `5_to_7` - `1_to_3`)

duration.model = lme4::lmer(data = f0_model_data, 
                    Hatched ~ Treatment * Day * Month + (1|Month))

duration_pairs = emmeans::emmeans(duration.model, 
                                  ~ Day | Month * Treatment) %>% 
  pairs()

as.data.frame(summary(duration_pairs))[c('Month', 
                                         'Treatment', 
                                         'contrast', 
                                         'estimate', 
                                         'SE')] %>% 
  mutate(Month = fct_relevel(Month, c("June", "August", "November")),
         estimate = estimate * -1) %>% #Flips sign to make contrast Long - Short
  ggplot(aes(x = Month, fill = Treatment, y = estimate)) + 
  geom_bar(stat = "identity", position = position_dodge(width = 0.9), colour = "black", size = 1) + 
  geom_errorbar(aes(ymin = estimate - SE, ymax = estimate + SE),
                width = 0.1, size = 1,
                position = position_dodge(width = 0.9)) + 
  geom_hline(yintercept = 0) + 
  scale_fill_manual(values = c("grey30", "white")) + 
  labs(x = "",
       y = "Duration Contrast \n Long - Short events") + 
  theme_pubr(base_size = 18)
```

```{r F1-duration-effects, fig.width=8, fig.height=8}
# How does heat wave duration affect transgenerational effects? Reaction norms shown below for effect size comparisons (heatwave - control) for different duration of parental exposure
# F1_data %>% 
#   dplyr::select(month, duration, off_temp, trait, difference, bca_ci_low, bca_ci_high) %>% 
#   mutate("ID" = paste(month, off_temp, trait, sep = "_"),
#          month = fct_relevel(month, c("June", "August", "November")),
#          duration = fct_relevel(duration, c("short", "long"))) %>% 
#   ggplot(aes(x = duration, y = difference, colour = month, group = ID)) + 
#   facet_wrap(trait~.) + 
#   geom_hline(yintercept = 0) + 
#   geom_line(size = 1, position = position_dodge(width = 0.1)) + 
#   geom_point(size = 2, position = position_dodge(width = 0.1)) + 
#   geom_errorbar(aes(ymin = bca_ci_low, ymax = bca_ci_high),
#                 size = 0.75, width = 0.1,
#                 position = position_dodge(width = 0.1)) + 
#   labs(x = "Parental Exposure Duration", 
#        y = "Effect Size (Hedge's g) \n Heatwave - Control") + 
#   ylim(-5,5) + 
#   theme_pubr(base_size = 18)
```


```{r sig-F1-duration-effects, fig.width=8, fig.height=8}
# #Pulls out reaction norms where there's a sign change (changes from positive, neutral, or negative between duration groups) 
# 
# duration_effects = F1_data %>% 
#   dplyr::select(month, duration, off_temp, trait, difference, bca_ci_low, bca_ci_high) %>% 
#   mutate("ID" = paste(month, off_temp, trait, sep = "_"),
#          month = fct_relevel(month, c("June", "August", "November")),
#          duration = fct_relevel(duration, c("short", "long"))) %>% 
#   group_by(ID, trait, duration) %>%  
#   mutate("zero_diff" = case_when(
#     sign(bca_ci_low) == sign(bca_ci_high) ~ "does not overlap zero",
#     sign(bca_ci_low) != sign(bca_ci_high) ~ "overlaps zero"
#   )) %>% 
#   ungroup(duration) %>% 
#   mutate("change" = case_when(
#     sign(difference)[1] == sign(difference)[2] & zero_diff[1] == zero_diff[2] ~ "Same",
#     sign(difference)[1] != sign(difference)[2] & zero_diff[1] == "overlaps zero" & zero_diff[2] == "overlaps zero" ~ "Same",
#     sign(difference)[1] != sign(difference)[2] & zero_diff[1] == zero_diff[2] & zero_diff[1] == "does not overlap zero" ~ "Different",
#     sign(difference)[1] != sign(difference)[2] & zero_diff[1] != zero_diff[2] ~ "Different",
#     sign(difference)[1] == sign(difference)[2] & zero_diff[1] != zero_diff[2] ~ "Different"
#   )) %>% 
#   arrange(month, off_temp, trait) %>% 
#   filter(change == "Different")
# 
# select_rnorms = duration_effects %>% 
#   dplyr::select(-duration, -difference, -bca_ci_low, -bca_ci_high, -ID, -zero_diff) %>% 
#   distinct()
# 
# sig_changes = F1_data %>% 
#   dplyr::select(month, duration, off_temp, trait, difference, bca_ci_low, bca_ci_high) %>% 
#   mutate("ID" = paste(month, off_temp, trait, sep = "_"),
#          month = fct_relevel(month, c("June", "August", "November")),
#          duration = fct_relevel(duration, c("short", "long"))) %>% 
#   filter(ID %in% select_rnorms$ID)
# 
# ggplot(sig_changes, aes(x = duration, y = difference, colour = month, group = ID)) + 
#   facet_wrap(trait~.) + 
#   geom_hline(yintercept = 0) + 
#   geom_line(size = 1, position = position_dodge(width = 0.1)) + 
#   geom_point(size = 2, position = position_dodge(width = 0.1)) + 
#   geom_errorbar(aes(ymin = bca_ci_low, ymax = bca_ci_high),
#                 size = 0.75, width = 0.1,
#                 position = position_dodge(width = 0.1)) + 
#   geom_label_repel(data = sig_changes %>% filter(duration == "long"), 
#                    aes(label = off_temp, 
#                        x = duration,
#                        y = difference, 
#                        color = month),
#                    box.padding = 0.5,
#                    nudge_x = 0.2,
#                    size = 7,
#                    show.legend=FALSE) + 
#   labs(x = "Parental Exposure Duration", 
#        y = "Effect Size (Hedge's g)\nHeatwave - Control") + 
#   ylim(-5,5) + 
#   theme_pubr(base_size = 18)
```


```{r, include = F}
# rdata = file_list[str_detect(file_list, pattern = ".RData")]
# f1_data = rdata[str_detect(rdata, pattern = "F0_", negate = T)] %>% 
#   str_split_fixed(pattern = ".RData", n = 2)
# f1_data = f1_data[,1]
# 
# plot_names = c()
# for(i in 1:length(f1_data)){
#   plot_name = paste(f1_data[i], "_plot", sep = "")
#   plot_names = c(plot_names, plot_name)
#   metric = str_split_fixed(plot_name, pattern = "_", n = 4)[,2]
#   
#   if(metric == "total"){
#     label = "Egg Production (per female)"
#   }
#   
#   if(metric == "rf"){
#     label = "Production (per female)"
#   }
#   
#   if(metric == "hs"){
#     label = "Hatching Success (%)"
#   }
#   
#   if(metric == "bs"){
#     label = "Body Size (mm)"
#   }
#   
#   assign(plot_name,
#          plot(get(f1_data[i]), 
#               effsize.markersize = 2,
#               axes.title.fontsize = 9,
#               tick.fontsize = 6,
#               swarmplot.params = param_list,
#               rawplot.ylabel = label,
#               theme = ggpubr::theme_pubr()))
# }
# 
# bs_plots = plot_names[str_detect(plot_names, pattern = "_bs_")]
# rf_plots = plot_names[str_detect(plot_names, pattern = "_rf_")]
# total_plots = plot_names[str_detect(plot_names, pattern = "_total_")]
# hs_plots = plot_names[str_detect(plot_names, pattern = "_hs_")]
```

```{r F1-body-size-eff-plots, fig.height=11, fig.width=8, include = F}
# ggarrange(June_bs_short_plot, June_bs_long_plot, 
#           August_bs_short_plot, August_bs_long_plot,
#           November_bs_short_plot, November_bs_long_plot,
#           ncol = 2, nrow = 3,
#           labels = "AUTO",
#           vjust = -0.2)
```

```{r F1-total-epr-eff-plots, fig.height=11, fig.width=8, include = F}
# ggarrange(June_total_short_plot, June_total_long_plot, 
#           August_total_short_plot, August_total_long_plot,
#           November_total_short_plot, November_total_long_plot,
#           ncol = 2, nrow = 3,
#           labels = "AUTO",
#           vjust = -0.2)
```

```{r F1-hatching-success-eff-plots, fig.height=11, fig.width=8, include = F}
# ggarrange(June_hs_short_plot, June_hs_long_plot, 
#           August_hs_short_plot, August_hs_long_plot,
#           November_hs_short_plot, November_hs_long_plot,
#           ncol = 2, nrow = 3,
#           labels = "AUTO",
#           vjust = -0.2)
```

```{r F1-production-eff-plots, fig.height=11, fig.width=8, include = F}
# ggarrange(June_rf_short_plot, June_rf_long_plot, 
#           August_rf_short_plot, August_rf_long_plot,
#           November_rf_short_plot, November_rf_long_plot,
#           ncol = 2, nrow = 3,
#           labels = "AUTO",
#           vjust = -0.2)
```
